Defining "Effective"
Or maybe just handwaving at it some
This post on Best Of A Great Lot is a part of a series on the subject of designing a new form of governance. Each piece aims to stand alone, but fits together on the Table of Contents.
What does “Effective” mean?
“Effective” is one of the more misunderstood terms out there, so I think it’s worth describing it. Many people conflate it with efficient. In particular, efficiency has at least one technical meaning within Economics - this paper claims 3 technical meanings - technical, productive, and allocative. None of these are identical to the common definition, and none of them are what we generally mean by “effective”.
There’s a classic paper in the computer science & technology world, titled “The Unreasonable Effectiveness of Data”, written by Googlers back in 2009, at the beginning of the Big Data movement. Some claim that this was in fact the beginning of the Big Data movement. The paper describes that with enough of our data, large companies can do surprising and amazing things. It made such a splash that there have been many copycats, papers titled “The Unreasonable Effectivess of” various things, like mathematics and graphical user interfaces and meaning and my personal favorite, turning it off and on again, from which we get this lovely summation of something being effective:
Often, the problem goes away.
The Unreasonable Effectiveness of Data is not about how to process data efficiently - i.e. quickly or cheaply. It’s about how you can do amazing things with a lot of data. Even more importantly, it’s about how this is not simply a scaling up of the things you can do with smaller amounts of data, but that there are categorically different capabilities that are unlocked by having enough data. This is, in effect, the same as saying that having the right view of the world can solve problems that you simply cannot solve without. If you try to build a rocket that can orbit Earth without understanding basic calculus or the equations of gravity, it’s unlikely that you’ll get 90% there and just need a little bit extra. You’ll just fail entirely. Correct math is unreasonably, perhaps even ridiculously, effective at unlocking technological advancements.
I’m attempting to use effective in a similar manner. Effective is when we can do a great job at accomplishing our goals. When we astonish ourselves and achieve the high bar that deeply satisfies our internal sense of having done well, rather than just settling for something easily achievable.
When it’s cheap and easy to do so, or when we use minimal energy, we can say it’s efficient. Efficiently accomplishing goals that aren’t good goals is a great way to waste a lot of time and money. For example, the most efficient way to make sure every student passes the test is to make the test easier. The most efficient test in that sense would be a roll call. Take this to extremes and you have Goodhart’s Law.
I also have to consider the Effective Altruism movement here, because it’s another obvious place where the word gets used. William MacAskill, who coined the term, described EA as “using evidence and reason to figure out how to benefit others as much as possible, and taking action on that basis”. It’s a common criticism that’s leveled at those interested in EA that the movement ignores what’s important in favor of what’s measurable. But the phrase “evidence and reason” doesn’t require that something be 100% data-driven, the way it’s often portrayed. Instead, it calls on us to use what evidence we have and also our ability to think to investigate what works and what does not. This is, I think, the core of “effective” - a focus on what works.
Here are some examples of effective, so we can be in the same emotional ballpark when we use the word. These are just vignettes, to set the flavor of it.
Israel was effective when it came to rolling out the first Covid-19 vaccinations to their population. Between December 19th of 2020 and March 9th of 2021, they vaccinated 56% of their population before they slowed down. They vaccinated every day of the week, and in many places 24 hours a day. They set out the goal of a high percentage of vaccination, and for 3 months, refused to let anything get in their way. Importantly, their success was not a result of a single small policy decision going right, but was the result of a dedicated effort at all levels along with many factors going well for them.
A large number of factors contributed to this early success… Israel’s small size (in terms of both area and population), a relatively young population, relatively warm weather in December 2020 … a tradition of effective cooperation between government, health plans, hospitals, and emergency care providers – particularly during national emergencies … the mobilization of special government funding for vaccine purchase and distribution, timely contracting for a large amount of vaccines relative to Israel’s population, the use of simple, clear and easily implementable criteria for determining who had priority for receiving vaccines in the early phases of the distribution process …
By comparison, the United States, despite starting at the same time, would not achieve that 56% mark until late July, nearly 5 months later. Was it possible for a larger country without warm weather to pull this off? It’s as easy for an observer to point to the lucky factors as it is to point to the systemic failures. But the same dynamics apply to systems attempting to take advantage of luck as to individuals: you can’t win if you don’t play. With the lottery, that’s a bad choice. But Israel showed you can win the luck factor if you have the other factors going your way.
The FAA, air traffic control, pilots, and commercial airlines, all working together as a system, is effective at preventing commercial plane crashes in the United States. The FAA points to their many safety programs for credit. Atul Gawande, in The Checklist Manifesto points to a history of checklist development and use among pilots, including during WWII and more recently.
Daniel Boorman from the Boeing Company in Seattle, Washington. He proved to be a veteran pilot who’d spent the last two decades developing checklists and flight deck controls for Boeing aircraft from the 747-400 forward… He is the lineal descendant of the pilots who came up with that first checklist for the B-17 bomber three-quarters of a century ago. He has studied thousands of crashes and near crashes over the years, and he has made a science of averting human error.
Perhaps, as we saw with Israel, it’s both and more. They have definitely succeeded. Wikipedia has a list of fatal accidents, and for the last twenty years there are around 25 total crashes, most of which are smaller planes, have only a few casualties, or both.
The Site Reliability Engineers working at Google and Netflix (and a few other major commercial web properties) are effective at keeping their main products, Google Search and Netflix videos, always up and always instantly responsive. Google search results return in milliseconds in dozens, perhaps hundreds of countries. Netflix videos start playing on users’ computers within tens of milliseconds in a similar number of places. Both of them almost never report outages, despite software outages being a regular occurrence even for most famous technology companies.
Montreal is effective at managing snow removal. Where other cities like Boston and Toronto plow it to the side of the road and manage to make sure their city doesn’t shut down after a snowstorm, but in so doing create barriers to accessibility for many people using modes of transport other than cars, Montreal actively removes snow to ensure that as many people as possible are able to go about their daily lives largely unaffected by snowstorms.
Living in Toronto, a snowstorm was treated as a shocking anomaly. Plows often cover sidewalks over for weeks, forcing pedestrians to walk into oncoming traffic and barring neighborhoods from people with mobility issues. But in Montreal, clear sidewalks ensure strollers and wheelchairs can keep rolling, and 75% of the bike lane network is cleared all winter. This means clear sightlines to oncoming traffic; it means bike rides with friends; it means more equitable access to personal mobility. With few barriers to travel, people across the city pour out into parks all winter to ski, sled, skate, or just bask in the open space.
When we talk about effective governance, we are talking about systems that allow for society to operate smoothly, that enable human flourishing. We’re talking about systems that easily identify problems that exist and discover and roll out solutions that actually solve those problems. Not all problems are tractable, but with sufficient cleverness, many are, and a society which solves for those has more resources to spare to ameliorate the more difficult ones.
Because our current government often seems so ineffective, our best minds are pulled into a war between the question of whether we should have more of it (because there are problems we don’t know any other way to solve) or less of it (because government as the solution often sucks). At its simplest, most twitterified, that fight comes down to the left arguing that government is good, and the right arguing that it’s bad. On the right, we have Grover Norquist’s quote:
I don’t want to abolish government. I simply want to reduce it to the size where I can drag it into the bathroom and drown it in the bathtub.
On the left, Oliver Wendell Holmes, former Justice of the United States Supreme Court, said:
Taxes are what we pay for a civilized society.
I don’t want to have to choose between more of an ineffective thing that we need, or more freedom from one. I want a system designed to successfully identify the problems that need to be solved and solve them well.